Teleoperation system improves robot touch sensing for delicate tasks
XRoboToolKit-T: Teleoperation with High Stability and Precision with Tactile Sensing for Contact-rich Manipulation
RoboticsHuman-Computer Interaction
Summary
Robots need good touch feedback to handle delicate tasks like gripping soft objects. The authors created a teleoperation system that uses tactile sensing to provide stable and precise control when a person remotely operates a robot. This system quickly interprets touch forces and helps adjust the robot's actions using both tactile data and task descriptions. They tested it on tricky tasks like squeezing a soft pipette and inserting a syringe, showing it collects better data and controls the robot more steadily than existing systems.
What this means in practice
- •For robotics engineers: Develop teleoperated robots that reliably manipulate soft or delicate objects using tactile feedback for precise force control.
- •For medical device developers: Create robotic training tools that use tactile sensing teleoperation to practice delicate procedures like syringe insertion.
Authors
Xiwen Dengxiong, Xueting Wang, Ke Jing, Rui Li, Yunbo Zhang
Abstract
Collecting high-quality robot data for contact-rich manipulation tasks is essential for enabling robots to acquire real-world skills. However, existing data collection solutions often lack the capability to obtain stable and high-frequency tactile feedback, limiting their effectiveness in contact-rich manipulation scenarios. In this work, we propose a versatile teleoperation system with tactile-driven assistance to enable high-frequency and stable contact-rich manipulation. The proposed XRoboToolKit-T teleoperation system incorporates a tactile-informed force control architecture, designed to ensure both stable and precise force control in contact-rich manipulation during teleoperation. The stabilizer haptic module rapidly analyzes the normal force distribution and infers pseudo shear force, enabling real-time tactile-based assistance during manipulation. The refiner haptic module integrates a vision-language-action model to predict and refine manipulation actions based on tactile sensing data and task descriptions. We apply the proposed teleoperation system to challenging contact-rich manipulation tasks, including grasping a deformable rubber pipette for liquid transfer and inserting a medical syringe into a vascular training pad, to demonstrate the effectiveness of tactile-informed force control. Furthermore, the system achieves higher data collection efficiency and improved manipulation stability compared to state-of-the-art teleoperation without tactile assistance.